As technology advances, the options for communications have become more varied. For example, in the last 30 years in the telecommunications industry, personal communications have evolved from a home having a single rotary dial telephone, to a home having multiple telephone, cable and/or fiber optic lines that accommodate both voice and data. Additionally cellular phones and Wi-Fi have added a mobile element to communications.
Until relatively recently, the primary use of cellular communications has been for voice communications between people. Even for this usage case, considerable optimization has been performed to carry the comparatively small amount of traffic in an optimal way, for instance the introduction of lower bit-rate codecs. In the last few years, the amount of information that needs to be transmitted has begun to increase dramatically with the increasing ubiquity of smart phones, wireless data services and users communicating with machines, particularly servers on the Internet. The next trend will likely be for mobile devices to be used to support machines communicating with other machines (e.g. “M2M”), for instance for remote monitoring and control and many other applications.
For these reasons, it is projected that, in the near future, there will be on the order of fifty billion connected communication devices. Of those, wireless devices are expected to interact with one another over a variety of air interfaces e.g., 3GPP-based radio interfaces, Bluetooth, Zigbee, etc, which may also use different bandwidths in the various frequency spectrums which have been allocated for wireless communications. With this growth in wireless communications, it is not surprising that almost all usable frequency bands have been allocated. Since radio spectrum has absolute limits in terms of usable frequencies, and the uses for radio communication continue to increase, there is a constant search for means to optimize the use of radio resources. One such approach is cognitive radio.
While there are several variants of cognitive radio, one way to describe this technology is that cognitive radio allows a device to sense the radio environment in which it is operating and then to use that information, and various input parameters (such as user behavior), to select a portion of the frequency spectrum to use for its communications, e.g., a portion of the spectrum that is both legitimate (e.g., in view of local or regional spectrum allocation rules) and that does not result in too much interference relative to other ongoing communications. This enables cognitive radio devices to use spectrum which, while nominally allocated for some purpose, is not currently being used in a particular location.
The research and development in cognitive radio technology is thus driven by, for example, (1) the recognition that radio resources are scarce and expensive and (2) a significant portion of the spectrum is relatively unused, but is “reserved” (like ham radio frequencies). Some challenges associated with the possibility of implementing cognitive radio systems include that: (1) the use of spectrum by primary users should be protected, (2) there may be legacy systems already in place that cannot be modified, (3) the usage of the spectrum in a particular area by primary users may vary as a factor of time, (4) as there may be multiple secondary users, the use of the spectrum between secondary users should be coordinated, (5) the available unused bandwidth may be discontinuous so that, for example, a user that needs 10 MHz of spectrum may need to “assemble” smaller chunks of spectrum instead of one continuous piece, (6) variations in the local radio environment, such as shadowing, can make it difficult for a secondary user radio to determine its radio situation relative to that of the primary user, e.g. “close but shadowed” versus “not-shadowed but far away”, and (7) noise uncertainty can make signal detection difficult and, below certain limits, effectively impossible.
Despite these, and other, challenges associated with implementing cognitive radio, the need to enable greater spectrum re-use is expected to drive further market-driven research in this area.